Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health.
Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University.
Am J Clin Nutr. 2019 Mar 1;109(3):517-525. doi: 10.1093/ajcn/nqy202.
Accurate assessment of dietary intake is essential, but self-report of dietary intake is prone to measurement error and bias. Discovering metabolic consequences of diets with lower compared with higher protein intake could elucidate new, objective biomarkers of protein intake.
The goal of this study was to identify serum metabolites associated with dietary protein intake.
Metabolites were measured with the use of untargeted, reverse-phase ultra-performance liquid chromatography-tandem mass spectrometry quantification in serum specimens collected at the 12-mo follow-up visit in the Modification of Diet in Renal Disease (MDRD) Study from 482 participants in study A (glomerular filtration rate: 25-55 mL · min-1 · 1.73 m-2) and 192 participants in study B (glomerular filtration rate: 13-24 mL · min-1 · 1.73 m-2). We used multivariable linear regression to test for differences in log-transformed metabolites (outcome) according to randomly assigned dietary protein intervention groups (exposure). Statistical significance was assessed at the Bonferroni-corrected threshold: 0.05/1193 = 4.2 × 10-5.
In study A, 130 metabolites (83 known from 28 distinct pathways, including 7 amino acid pathways; 47 unknown) were significantly different between participants randomly assigned to the low-protein diet compared with the moderate-protein diet. In study B, 32 metabolites (22 known from 8 distinct pathways, including 4 amino acid pathways; 10 unknown) were significantly different between participants randomly assigned to the very-low-protein diet compared with the low-protein diet. A total of 11 known metabolites were significantly associated with protein intake in the same direction in both studies A and B: 3-methylhistidine, N-acetyl-3-methylhistidine, xanthurenate, isovalerylcarnitine, creatine, kynurenate, 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPE (P-16:0/20:4), 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4), 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4), sulfate, and γ-glutamylalanine.
Among patients with chronic kidney disease, an untargeted serum metabolomics platform identified multiple pathways and metabolites associated with dietary protein intake. Further research is necessary to characterize unknown compounds and to examine these metabolites in association with dietary protein intake among individuals without kidney disease.This trial was registered at clinicaltrials.gov as NCT03202914.
准确评估膳食摄入量至关重要，但膳食摄入量的自我报告容易出现测量误差和偏倚。发现与蛋白质摄入量较高相比，蛋白质摄入量较低的饮食的代谢后果可以阐明新的、客观的蛋白质摄入量生物标志物。
本研究的目的是确定与膳食蛋白质摄入量相关的血清代谢物。
在 Modification of Diet in Renal Disease (MDRD) 研究的 12 个月随访中，使用非靶向、反相超高效液相色谱-串联质谱定量法测量了来自研究 A（肾小球滤过率：25-55 mL·min-1·1.73 m-2）的 482 名参与者和研究 B（肾小球滤过率：13-24 mL·min-1·1.73 m-2）的 192 名参与者的血清样本中的代谢物。我们使用多变量线性回归来测试根据随机分配的膳食蛋白质干预组（暴露）的 log 转换代谢物（结果）的差异。使用 Bonferroni 校正的阈值评估统计学意义：0.05/1193=4.2×10-5。
在研究 A 中，与随机分配到低蛋白饮食的参与者相比，随机分配到中蛋白饮食的参与者之间有 130 种代谢物（83 种来自 28 种不同途径，包括 7 种氨基酸途径；47 种未知）存在显著差异。在研究 B 中，与随机分配到低蛋白饮食的参与者相比，随机分配到极低蛋白饮食的参与者之间有 32 种代谢物（22 种来自 8 种不同途径，包括 4 种氨基酸途径；10 种未知）存在显著差异。在这两项研究中，共有 11 种已知代谢物与蛋白质摄入量呈相同方向显著相关：3-甲基组氨酸、N-乙酰-3-甲基组氨酸、黄嘌呤酸、异戊酰肉碱、肌酸、犬尿氨酸、1-(1-烯基-棕榈酰)-2-花生四烯酰-GPE (P-16:0/20:4)、1-(1-烯基-硬脂酰)-2-花生四烯酰-GPE (P-18:0/20:4)、1-(1-烯基-棕榈酰)-2-花生四烯酰-GPC (P-16:0/20:4)、硫酸根和γ-谷氨酰丙氨酸。
在患有慢性肾脏病的患者中，一种非靶向的血清代谢组学平台确定了多个与膳食蛋白质摄入量相关的途径和代谢物。有必要进一步研究以描述未知化合物，并在没有肾脏疾病的个体中检查这些代谢物与膳食蛋白质摄入量的关联。本试验在 clinicaltrials.gov 上注册为 NCT03202914。
